Instructions to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF",
	filename="LLama-3.2-3B-Lollms-Finetuned-GGUF.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Use Docker

docker model run hf.co/ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Ollama
How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Ollama:
```
ollama run hf.co/ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
```

Unsloth Studio new

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF to start chatting

Pi new

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Docker Model Runner:
```
docker model run hf.co/ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0
```

Lemonade

How to use ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF:Q8_0

Run and chat with the model

lemonade run user.LLama-3.2-3B-Lollms-Finetuned-GGUF-Q8_0

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This is a finetune of the Llama 3.2 3B with the default lollms style. The finetune used the lollms aware database that contains information about lollms. Using this model in lollms reduces the need to add context information to the prompt.

Downloads last month: 44

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

8-bit

Model tree for ParisNeo/LLama-3.2-3B-Lollms-Finetuned-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

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LLama-3.2-3B-Lollms-Finetuned-GGUF

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